Compliance

score = client.get_readiness_score()

{
  "total_score": 85,
  "max_score": 120,
  "rating": "good",
  "components": [
    {"name": "agent_registry", "score": 20, "max_score": 20, "status": "complete"},
    {"name": "risk_classification", "score": 15, "max_score": 20, "status": "partial"},
    {"name": "governance_policies", "score": 15, "max_score": 15, "status": "complete"},
    {"name": "human_oversight", "score": 15, "max_score": 20, "status": "partial"},
    {"name": "audit_trail", "score": 15, "max_score": 15, "status": "complete"},
    {"name": "fria_completion", "score": 5, "max_score": 20, "status": "partial"},
    {"name": "incident_readiness", "score": 0, "max_score": 10, "status": "missing"}
  ],
  "recommendations": [
    "Classify the remaining 2 agents with a risk level",
    "Complete FRIA for agents: hr-screener, loan-evaluator"
  ]
}

fria = client.get_fria("agt_abc123")

{
  "id": "fria_abc123",
  "agent_id": "agt_abc123",
  "version": 1,
  "status": "submitted",
  "processes": "...",
  "frequency_and_scope": "...",
  "categories_of_persons": "...",
  "specific_risks": "...",
  "human_oversight_measures": "...",
  "risk_mitigation_measures": "..."
}

card = client.get_transparency_card("agt_abc123")

{
  "agent_id": "agt_abc123",
  "agent_name": "production-summarizer",
  "purpose": "Summarizes customer support tickets for the support team",
  "framework": "crewai",
  "model_provider": "openai",
  "model_name": "gpt-4o",
  "risk_level": "limited",
  "capabilities": [
    "Text summarization",
    "Ticket categorization"
  ],
  "limitations": [
    "May miss context in multi-language tickets",
    "Does not handle image attachments"
  ],
  "human_oversight": "Output reviewed by support team leads before customer communication",
  "data_sources": "Customer support ticket text (no PII stored)",
  "generated_at": "2026-03-17T10:00:00Z"
}

incident = client.create_incident(
    agent_id="agt_abc123",
    title="Discriminatory output in hiring agent",
    severity="high",
    description="The HR screening agent systematically ranked female candidates lower...",
    impact="Approximately 120 job applicants may have been affected.",
    immediate_actions="Agent suspended immediately.",
    root_cause="Training data contained historical hiring bias.",
    affected_persons_count=120,
    involves_health_or_death=False,
)

incidents = client.list_incidents(status="open", limit=20)

client.update_incident(42, status="resolved", root_cause="Training data bias corrected")